Showing posts with label Digital Debt. Show all posts
Showing posts with label Digital Debt. Show all posts

Daily Tech Digest - August 16, 2025


Quote for the day:

"Develop success from failures. Discouragement and failure are two of the surest stepping stones to success." -- Dale Carnegie


Digital Debt Is the New Technical Debt (And It’s Worse)

Digital debt doesn’t just slow down technology. It slows down business decision-making and strategic execution. Decision-Making Friction: Simple business questions require data from multiple systems. “What’s our customer lifetime value?” becomes a three-week research project because customer data lives in six different platforms with inconsistent definitions. Campaign Launch Complexity: Marketing campaigns that should take two weeks to launch require six weeks of coordination across platforms. Not because the campaign is complex, but because the digital infrastructure is fragmented. Customer Experience Inconsistency: Customers encounter different branding, messaging, and functionality depending on which digital touchpoint they use. Support teams can’t access complete customer histories because data is distributed across systems. Innovation Paralysis: New initiatives get delayed because teams spend time coordinating existing systems rather than building new capabilities. Digital debt creates a gravitational pull that keeps organizations focused on maintenance rather than innovation. ... Digital debt is more dangerous than technical debt because it’s harder to see and affects more stakeholders. Technical debt slows down development teams. Digital debt slows down entire organizations.


Rising OT threats put critical infrastructure at risk

Attackers are exploiting a critical remote code execution (RCE) vulnerability in the Erlang programming language's Open Telecom Platform, widely used in OT networks and critical infrastructure. The flaw enables unauthenticated users to execute commands through SSH connection protocol messages that should be processed only after authentication. Researchers from Palo Alto Networks' Unit 42 said they have observed more than 3,300 exploitation attempts since May 1, with about 70% targeting OT networks across healthcare, agriculture, media and high-tech sectors. Experts urged affected organizations to patch immediately, calling it a top priority for any security team defending an OT network. The flaw, which has a CVSS score of 10, could enable an attacker to gain full control over a system and disrupt connected systems -- particularly worrisome in critical infrastructure. ... Despite its complex cryptography, the protocol contains design flaws that could enable attackers to bypass authentication and exploit outdated encryption standards. Researcher Tom Tervoort, a security specialist at Netherlands-based security company Secura, identified issues affecting at least seven different products, resulting in the issuing of three CVEs.


Why Tech Debt is Eating Your ROI (and How To Fix It)

Regardless of industry or specific AI efforts, these frustrations seem to boil down to the same culprit. Their AI initiatives continue to stumble over decades of accumulated tech debt. Part of the reason is despite the hype, most organizations use AI — let’s say, timidly. Fewer than half employ it for predictive maintenance or detecting network anomalies. Fewer than a third use it for root-cause analysis or intelligent ticket routing. Why such hesitation? Because implementing AI effectively means confronting all the messiness that came before. It means admitting our tech environments need a serious cleanup before adding another layer of complexity. Tech complexity has become a monster. This mess came from years of bolting on new systems without retiring old ones. Some IT professionals point to redundant applications as a major source of wasted budget and others blame overprovisioning in the cloud — the digital equivalent of paying rent on empty apartments. ... IT teams admit something that, to me, is alarming: Their infrastructure has grown so tangled they can no longer maintain basic security practices. Let that sink in. Companies with eight-figure tech budgets can’t reliably patch vulnerable systems or implement fundamental security controls. No one builds silos deliberately. Silos emerge from organizational boundaries, competing priorities and the way we fund and manage projects. 


Ready on paper, not in practice: The incident response gap in Australian organisations

The truth is, security teams often build their plans around assumptions rather than real-world threats and trends. That gap becomes painfully obvious during an actual incident, when organisations realise they aren't adequately prepared to respond. Recent findings of a Semperis study titled The State of Enterprise Cyber Crisis Readiness revealed a strong disconnect between organisations' perceived readiness to respond to a cyber crisis and their actual performance. The study also showed that cyber incident response plans are being implemented and regularly tested, but not broadly. In a real-world crisis, too many teams are still operating in silos. ... A robust, integrated, and well-practiced cyber crisis response plan is paramount for cyber and business resilience. After all, the faster you can respond and recover, the less severe the financial impact of a cyberattack will be. Organisations can increase their agility by conducting tabletop exercises that simulate attacks. By practicing incident response regularly and introducing a range of new scenarios of varying complexity, organisations can train for the real thing, which can often be unpredictable. Security teams can continually adapt their response plans based on the lessons learned during these exercises, and any new emerging cyber threats.


Quantum Threat Is Real: Act Now with Post Quantum Cryptography

Some of the common types of encryption we use today include RSA (Rivest-Shamir-Adleman), ECC (Elliptic Curve Cryptography), and DH (Diffie-Hellman Key Exchange). The first two are asymmetric types of encryption. The third is a useful fillip to the first to establish secure communication, with secure key exchange. RSA relies on very large integers, and ECC, on very hard-to-solve math problems. As can be imagined, these cannot be solved with traditional computing. ... Cybercriminals think long-term. They are well aware that quantum computing is still some time away. But that doesn’t stop them from stealing encrypted information. Why? They will store it securely until quantum computing becomes readily available; then they will decrypt it. The impending arrival of quantum computers has set the cat amongst the pigeons. ... Blockchain is not unhackable, but it is difficult to hack. A bunch of cryptographic algorithms keep it secure. These include SHA-256 (Secure Hash Algorithm 256-bit) and ECDSA (Elliptic Curve Digital Signature Algorithm). Today, cybercriminals might not attempt to target blockchains and steal crypto. But tomorrow, with the availability of a quantum computer, the crypto vault can be broken into, without trouble. ... We keep saying that quantum computing and quantum computing-enabled threats are still some time away. And, this is true. But when the technology is here, it will evolve and gain traction. 


Cultivating product thinking in your engineering team

The most common trap you’ll encounter is what’s called the “feature factory.” This is a development model where engineers are simply handed a list of features to build, without context. They’re measured on velocity and output, not on the value their work creates. This can be comfortable for some – it’s a clear path with measurable metrics – but it’s also a surefire way to kill innovation and engagement. ... First and foremost, you need to provide context, and you need to do so early and often. Don’t just hand a Jira ticket to an engineer. Before a sprint starts, take the time to walk through the “what,” the “why,” and the “who.” Explain the market research that led to this feature request, share customer feedback that highlights the problem, and introduce them to the personas you’re building for. A quick 15-minute session at the start of a sprint can make a world of difference. You should also give engineers a seat at the table. Invite them to meetings where product managers are discussing strategy and customer feedback. They don’t just need to hear the final decision; they need to be a part of the conversation that leads to it. When an engineer hears a customer’s frustration firsthand, they gain a level of empathy that a written user story can never provide. They’ll also bring a unique perspective to the table, challenging assumptions and offering technical solutions you may not have considered.


Adapting to New Cloud Security Challenges

While the essence of Non-Human Identities and their secret management is acknowledged, many organizations still grapple with the efficient implementation of these practices. Some stumble upon the over-reliance on traditional security measures, thereby failing to adopt newer, more effective strategies that incorporate NHI management. Others struggle with time and resource constraints, devoid of efficient automation mechanisms – a crucial aspect for proficient NHI management. The disconnect between security and R&D teams often results in fractured efforts, leading to potential security gaps, breaches, and data leaks. ... With more organizations migrate to the cloud and with the rise of machine identities and secret management, the future of cloud security has been redefined. It is no longer solely about the protection from known threats but now involves proactive strategies to anticipate and mitigate potential future risks. This shift necessitates organizations to rethink their approach to cybersecurity, with a keen focus on NHIs and Secrets Security Management. It requires an integrated endeavor, involving CISOs, cybersecurity professionals, and R&D teams, along with the use of scalable and innovative platforms. Thought leaders in the data field continue to emphasize the importance of robust NHI management as vital to the future of cybersecurity, driving the message home for businesses of all sizes and across all industries.


Why IT Modernization Occurs at the Intersection of People and Data

A mandate for IT modernization doesn’t always mean the team has the complete expertise necessary to complete that mandate. It may take some time to arm the team with the correct knowledge to support modernization. Let’s take data analytics, for example. Many modern data analytics solutions, armed with AI, now allow teams to deliver natural language prompts that can retrieve the data necessary to inform strategic modernization initiatives without having to write expert-level SQL. While this lessens the need for writing scripts, IT leaders must still ensure their teams have the right expertise to construct the correct prompts. This could mean training on correct terms for presenting data and/or manipulating data, along with knowing in what circumstances to access that data. Having a well-informed and educated team will be especially important after modernization efforts are underway. ... One of the most important steps to IT modernization is arming your IT teams with a complete picture of the current IT infrastructure. It’s equivalent to giving them a full map before embarking on their modernization journey. In many situations, an ideal starting point is to ensure that any documentation, ER diagrams, and architectural diagrams are collected into a single repository and reviewed. Then, the IT teams use an observability solution that integrates with every part of the enterprise infrastructure to show each team how every part of it works together. 


Cyber Resilience Must Become The Third Pillar Of Security Strategy

For years, enterprise security has been built around two main pillars: prevention and detection. Firewalls, endpoint protection, and intrusion detection systems all aim to stop attackers before they do damage. But as threats grow more sophisticated, it’s clear that this isn’t enough. ... The shift to cloud computing has created dangerous assumptions. Many organizations believe that moving workloads to AWS, Azure, or Google Cloud means the provider “takes care of security.” ... Effective resilience starts with rethinking backup as more than a compliance checkbox. Immutable, air-gapped copies prevent attackers from tampering with recovery points. Built-in threat detection can spot ransomware or other malicious activity before it spreads. But technology alone isn’t enough. Mariappan urges leaders to identify the “minimum viable business” — the essential applications, accounts, and configurations required to function after an incident. Recovery strategies should be built around restoring these first to reduce downtime and financial impact. She also stresses the importance of limiting the blast radius. In a cloud context, that might mean segmenting workloads, isolating credentials, or designing architectures that prevent a single compromised account from jeopardizing an entire environment.


Breaking Systems to Build Better Ones: How AI is Reshaping Chaos Engineering

While AI dominates technical discussions across industries, Andrus maintains a pragmatic perspective on its role in system reliability. “If Skynet comes about tomorrow, it’s going to fail in three days. So I’m not worried about the AI apocalypse, because AI isn’t going to be able to build and maintain and run reliable systems.” The fundamental challenge lies in the nature of distributed systems versus AI capabilities. “A lot of the LLMs and a lot of what we talk about in the AI world is really non deterministic, and when we’re talking about distributed systems, we care about it working correctly every time, not just most of the time.” However, Andrus sees valuable applications for AI in specific areas. AI excels at providing suggestions and guidance rather than making deterministic decisions. ... Despite its name, chaos engineering represents the opposite of chaotic approaches to system reliability. “Chaos engineering is a bit of a misnomer. You know, a lot of people think, Oh, we’re going to go cause chaos and see what happens, and it’s the opposite. We want to engineer the chaos out of our systems.” This systematic approach to understanding system behavior under stress provides the foundation for building more resilient infrastructure. As AI-generated code increases system complexity, the need for comprehensive reliability testing becomes even more critical. 

Daily Tech Digest - May 04, 2024

We Need an Updated Strategy to Secure Identities

Identity needs to be foremost in any security strategy since we’ve ample evidence it remains a frequent target. Most breaches today originate with identity through human error, social engineering or phishing. Solutions providers like Cisco are offering cybersecurity tools that bring together the worlds of identity, networking and security to detect and prevent these identity threats. Solutions like Cisco Duo, Cisco Identity Intelligence, and Cisco Secure Access can minimize exposure. Cisco Duo protects access to applications and data with strong multi-factor authentication, while Cisco Secure Access emphasizes secure remote connectivity to prevent unsanctioned users from gaining access. Additionally, Cisco Identity Intelligence uses AI to analyze user behavior and identity data to proactively clean up vulnerable identities and to detect identity-based security threats. Most organizations use a variety of solutions collected over the years that now reside in the cloud, on premises or in hybrid environments. That’s why an platform approach is so important. It also needs to be easy to deploy and easy for end users to manage.


What is cybersecurity mesh architecture (CSMA)?

Cybersecurity mesh architecture (CSMA) is a set of organizing principles used to create an effective security framework. Using a CSMA approach means designing a security architecture that is composable and scalable with easily extensible interfaces, a common data schema and well-defined interfaces and APIs for interoperability. ... A CMSA proactively blocks attacks through a variety of controls and system design principles. Leveraging advanced machine learning for anomaly detection and employing Secure Access Service Edge (SASE) for dynamic, secure cloud access, CSMA ensures robust encryption standards for data at rest and in transit. Network segmentation and micro-segmentation, paired with continuous authentication and strict authorization, can restrict lateral movement. These components, alongside continuous compliance monitoring and risk management tools, orchestrate a multi-layered defense strategy that preempts cyber threats by dynamically adapting to the evolving security landscape and ensuring continuous protection against potential vulnerabilities and unauthorized access attempts.


Managing Digital Debt: Artificial Intelligence And Human Sustainability

Digital debt represents the time and energy spent managing digital tasks, impeding core job responsibilities. At the same time, while their employees are trying to manage digital communication and creative thinking, organizations are constantly chasing after cutting-edge software solutions to stay ahead in the competitive market. In that race, they are piling up their tech balance sheets with the lesser-known but omnipresent “technical debt.” ... Leaders face the daunting task of balancing short-term gains with long-term sustainability, promoting accountability and continuous improvement within their teams. Increasing digital debt hampers organizational agility, raises maintenance costs, heightens the risk of failures and diminishes employee morale, highlighting the imperative for effective leadership in managing debt accumulation. Rather than chasing the newest trends and platforms, leaders should focus on their employees and the ease of doing business not only for the customers but also employees. 


Enhancing Developer Experience for Creating AI Applications

Kuzniak mentioned that enhancing the developer experience is as crucial as improving user experience. Their goal is to eliminate any obstacles in the implementation process, ensuring a seamless and efficient development flow. They envisioned the ideal developer experience, focusing on simplicity and effectiveness: For the AI implementation, we’ve established key principles:Simplicity: enable implementation with just one line of code. Immediate Accessibility: allow real-time access to prompts without the need for deployment. Security and Quality: integrate security and quality management by design. Cost Efficiency: design cost management and thresholds into the system by default. Kuzniak mentioned that their organizational structures are evolving in the face of the technology landscapes. The traditional cross-functional teams comprising product managers, designers, and developers, while still relevant, may not always be the optimal setup for AI projects, as he explained: We should consider alternative organizational models.  


Code faster with generative AI, but beware the risks when you do

"Our experience is that [GenAI-powered] software coding tools aren't as security-aware and [attuned with] security coding practices," he said. For instance, developers who work for organizations in a regulated or data-sensitive environment may have to adhere to additional security practices and controls as part of their software delivery processes. Using a coding assistant can double productivity, but developers need to ask if they can adequately test the code and fulfill the quality requirements along the pipeline, he noted. It's a double-edged sword: Organizations must look at how GenAI can augment their coding practices so the products they develop are more secure, and -- at the same time -- how the AI brings added security risks with new attack vectors and vulnerabilities. Because it delivers significant scale, GenAI amplifies everything an organization does, including the associated risks, Shaw noted. A lot more code can be generated with it, which also means the number of potential risks increases exponentially.


It's the End of the Entrepreneurial Era As We Know It

Today, being an entrepreneur seems to be as easy as twiddling your thumbs and clicking (or swiping) on a few buttons on an app on a smartphone. Hard work? Unlikely! Just click the right settings or prompts and 'Voila!' let the machine do the hard work! Humans were born with the anatomy and physique to be hunters, gatherers, lumberjacks, climbers, and runners. We were blessed to be physically active and agile. Unfortunately, the human race has just been through an entire century of changing those mannerisms into becoming desk-bound, delivery-service complacent hermits. ... Is a person truly an entrepreneur, when all they did was click a button and the rest of it was automated? If they built the hardware, software, and automation themselves? Then in my eyes, it's clearly entrepreneurial. But if another created the machine and they used it, are they really an entrepreneur? Having produced and directed many TV shows exploring and exposing advanced tech and innovation positively, I am clearly bullish on our technologically supercharged future. 


'Architecture by conference' is a really bad idea

The role of a generative AI architect should go beyond merely applying existing technologies; it should involve pioneering new methodologies and pushing the boundaries of what’s possible. As leaders, we must foster a culture that not only encourages innovation but actively rewards it. Are we questioning established norms and continuously seeking opportunities to improve and innovate? Are we blindly following other people’s approaches to completely different business problems? It’s time to stop imitating architectural processes from hyperscaler conferences or reusing frameworks, spreadsheets, and slides developed for another project by whatever consulting firm. You need to get smart quickly and stop copying off other people’s papers. The journey toward exceptional generative AI architecture for use in or out of the cloud is challenging yet crucial. It requires a break from tradition, a commitment to deep customization, and a resolve to innovate. I wish I could tell you this is easy, but we’re about to embark on building core IT systems that will define the business’s value.


The slow burn of data egress fees

Despite the financial and technical barriers, some companies are undertaking “cloud repatriation,” where workloads are moved off the cloud and back on-premises. A UK-based study conducted by Citrix found that, of the 350 companies surveyed, a quarter have moved half or more of their cloud-based workloads back to their own infrastructure, or are considering doing so. Among the list of motivations, Citrix noted unexpected costs (33 percent of respondents), performance issues, security concerns, compatibility issues, and service downtime. Twenty-two percent listed financial concerns as the main motivation for repatriation. Fifty percent of respondents identified data transfer fees as a significant contributing factor to unexpected cloud costs. Omdia’s Hahn has seen more repatriation of late. “In the last year or two years, companies have been a lot more selective,” he says. “There used to be a trend of cloud first, cloud all in, but now it seems more like companies think, ‘okay, we’ve got these workloads, some of them make sense to go to the cloud, some make sense to go on-premise.’”


Coaching your IT team for change: 9 tips

“You need a clearly articulated ‘why,’” says Sharon Mandell, CIO of Juniper Networks. “Then you need to communicate, communicate, communicate. And if you think you’ve communicated enough, communicate some more.” People will still push back, she says. There will always be people who say, “We’ve always done it this way and it works just fine.” ... “Changes are most accepted if they are tied to mission and purpose,” says Jennifer Dulski, CEO and founder of software company Rising Team. “Every company has a vision, a mission, a set of values,” she says. If you tie the change to that, it won’t feel arbitrary or unnecessary. “Start by grounding it in your mission,” she says. “And be clear about how the benefits are tied to the mission.” Paulo Gardini Miguel, director of technology at The CTO Club, agrees. “Begin by painting the big picture,” he says. “Explain the rationale behind the change and demonstrate how it aligns with the organization’s goals. Highlight the benefits of the change for the team, the company, and the customers.” Whenever possible, leaders go deeper than the company’s stated mission, Dulski says.


Clean Data, Trusted Model: Ensure Good Data Hygiene for Your LLMs

“Garbage in, garbage out” has never rung truer than with LLMs. Just because you have vast troves of data to train a model doesn’t mean you should do so. Whatever data you use should have a reasonable and defined purpose. The fact is, some data is just too risky to input into a model. Some can carry significant risks, such as privacy violations or biases. It is crucial to establish a robust data sanitization process to filter out such problematic data points and ensure the integrity and fairness of the model’s predictions. In this era of data-driven decision-making, the quality and suitability of the inputs are just as vital as the sophistication of the models themselves. One method rising in popularity is adversarial testing on models. Just as selecting clean and purposeful data is vital for model training, assessing the model’s performance and robustness is equally crucial in the development and deployment stages. These evaluations help detect potential biases, vulnerabilities or unintended consequences that may arise from the model’s predictions. There’s already a growing market of startups specializing in providing services for precisely this purpose. 



Quote for the day:

''Leadership occurs any time you attempt to influence the thinking, development of beliefs of somebody else.'' -- Dr. Ken Blanchard